Chen, M. Y., W. Shi, P. P. Xie, V. B. S. Silva, V. E. Kousky, R. W. Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132. |
Ding, Y. H., 1994: Monsoons over China. Springer, Netherlands, 420 pp, https://doi.org/10.1007/978-94-015-8302-2. |
Ding, Y. H., 2007: The variability of the Asian summer monsoon. J. Meteor. Soc. Japan, 85B, 21−54, https://doi.org/10.2151/jmsj.85B.21. |
Ding, Y. H., P. Liang, Y. J. Liu, and Y. C. Zhang, 2020: Multiscale variability of Meiyu and its prediction: A new review. J. Geophys. Res., 125, e2019JD031496, https://doi.org/10.1029/2019JD031496. |
Ding, Y. H., Y. Y. Liu, and Z. Z. Hu, 2021: The record-breaking mei-yu in 2020 and associated atmospheric circulation and tropical SST anomalies. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-021-0361-2. |
Gushchina, D., and B. Dewitte, 2012: Intraseasonal tropical atmospheric variability associated with the two flavors of El Niño. Mon. Wea. Rev., 140, 3669−3681, https://doi.org/10.1175/MWR-D-11-00267.1. |
Hendon, H. H., C. D. Zhang, and J. D. Glick, 1999: Interannual variation of the Madden-Julian Oscillation during Austral summer. J. Climate, 12, 2538−2550, https://doi.org/10.1175/1520-0442(1999)012<2538:IVOTMJ>2.0.CO;2. |
Hu, Z.-Z., S. Yang, and R. G. Wu, 2003: Long‐term climate variations in China and global warming signals. J. Geophys. Res., 108, 4614, https://doi.org/10.1029/2003JD003651. |
Hu, Z.-Z., A. Kumar, B. Jha, J. Zhu, and B. H. Huang, 2017: Persistence and predictions of the remarkable warm anomaly in the northeastern Pacific Ocean during 2014−16. J. Climate, 30, 689−702, https://doi.org/10.1175/JCLI-D-16-0348.1. |
Hu, Z.-Z., A. Kumar, B. Jha, and B. Y. Huang, 2020: How much of monthly mean precipitation variability over global land is associated with SST anomalies? Climate Dyn., 54, 701−712, https://doi.org/10.1007/s00382-019-05023-5. |
Huang, B. Y., and Coauthors, 2017: Extended reconstructed sea surface temperature, version 5(ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30(20), 8179−8205, https://doi.org/10.1175/JCLI-D-16-0836.1. |
Huang, N. E., and Z. H. Wu, 2008: A review on Hilbert-Huang Transform: Method and its applications to geophysical studies. Rev. Geophys., 46, RG2006, https://doi.org/10.1029/2007RG000228. |
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437−472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2. |
Kim, H.-M., 2017: The impact of the mean moisture bias on the key physics of MJO propagation in the ECMWF reforecast. J. Geophys. Res., 122, 7772−7784, https://doi.org/10.1002/2017JD027005. |
L’Heureux, M., E. Becker, M. S. Halpert, Z.-Z. Hu, K. MacRitchie, and M. Tippett, 2021: ENSO and the tropical Pacific. [In “State of the Climate in 2020”]. Bull. Amer. Meteor. Soc., 102. |
Liang, P., and Y. H. Ding, 2012: Climatologic characteristics of the intraseasonal oscillation of East Asian Meiyu. Acta Meteorologica Sinica, 70, 418−435, https://doi.org/10.11676/qxxb2012.036. (in Chinese with English abstract |
Liang, P., and H. Lin, 2018: Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system. Climate Dyn., 50, 1007−1022, https://doi.org/10.1007/s00382-017-3658-1. |
Liang, P., X. Tang, J.-H. He, and L.-X. Chen, 2008: An East Asian subtropical summer monsoon index defined by moisture transport. Journal of Tropical Meteorology, 14, 61−64. |
Liang, P., Z.-Z. Hu, Y. Y. Liu, X. Yuan, X. F. Li, and X. W. Jiang, 2019: Challenges in predicting and simulating summer rainfall in the eastern China. Climate Dyn., 52, 2217−2233, https://doi.org/10.1007/s00382-018-4256-6. |
Liang, P., G. T. Dong, H. Q. Zhang, M. Zhao, and Y. Ma, 2020: Atmospheric rivers associated with summer heavy rainfall over the Yangtze Plain. Journal of Southern Hemisphere Earth Systems Science, 70, 54−69, https://doi.org/10.1071/ES19028. |
Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Sci., 77, 1275−1277. |
Lim, Y., S.-W. Son, and D. Kim, 2018: MJO prediction skill of the subseasonal-to-seasonal prediction models. J. Climate, 31(10), 4075−4094, https://doi.org/10.1175/JCLI-D-17-0545.1. |
Liu, B. Y., Y. H. Yan, C. W. Zhu, S. M. Ma, and J. Y. Li, 2020: Record-breaking Meiyu rainfall around the Yangtze River in 2020 regulated by the subseasonal phase transition of the North Atlantic oscillation. Geophys. Res. Lett., 47, e2020GL090342, https://doi.org/10.1029/2020GL090342. |
Liu, Y. Y., Y. G. Wang, and Z. J. Ke, 2021: Characteristics and possible causes for the climate anomalies over China in summer 2020. Meteorological Monthly, 47(1), 117−126, https://doi.org/10.7519/j.issn.1000-0526.2021.01.011. (in Chinese with English abstract |
National Academies of Sciences (NAS), Engineering, and Medicine (Baltimore), 2016: Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts. National Academies Press. |
Ninomiya, K., and H. Muraki, 1986: Large-scale circulations over East Asia during baiu period of 1979. J. Meteor. Soc. Japan, 64, 409−429, https://doi.org/10.2151/jmsj1965.64.3_409. |
Nitta, T., 1986: Long-term variations of cloud amount in the western Pacific region. J. Meteor. Soc. Japan, 64, 373−390, https://doi.org/10.2151/jmsj1965.64.3_373. |
Nitta, T., and Z.-Z. Hu, 1996: Summer climate variability in China and its association with 500 hPa height and tropical convection. J. Meteor. Soc. Japan, 74, 425−445, https://doi.org/10.2151/jmsj1965.74.4_425. |
Oh, T. H., W. T. Kwon, and S. B. Ryoo, 1997: Review of the researches on changma and future observational study (KORMEX). Adv. Atmos. Sci., 14, 207−222, https://doi.org/10.1007/s00376-997-0020-2. |
Peng, P., A. Kumar, A. Barnston, and L. Goddard, 2000: Simulation skills of the SST-forced global climate variability of the NCEP-MRF9 andScripps/MPI ECHAM3 models. J. Climate, 13, 3657−3679. |
Qian, D. L., and Z. Y. Guan, 2019: Impacts of tropical Indian Ocean sea surface temperature anomalies on the variation of western Pacific subtropical high in the summer: Dependent and independent of ENSO. Acta Meteorologica Sinica, 77(3), 442−455, https://doi.org/10.11676/qxxb2019.030. (in Chinese with English abstract |
Saha, S., and Coauthors, 2014: The NCEP climate forecast system version 2. J. Climate, 27, 2185−2208, https://doi.org/10.1175/JCLI-D-12-00823.1. |
Saji, N. H., and T. Yamagata, 2003: Possible impacts of Indian Ocean dipole mode events on global climate. Climate Research, 25, 151−169, https://doi.org/10.3354/cr025151. |
Slingo, J. M., D. P. Rowell, K. R. Sperber, and F. Nortley, 1999: On the predictability of the interannual behaviour of the Madden-Julian oscillation and its relationship with el Nin?o Quart. J. Roy. Meteor. Soc., 125, 583−609, https://doi.org/10.1002/qj.49712555411. |
Takaya, Y., I. Ishikawa, C. Kobayashi, H. Endo, and T. Ose, 2020: Enhanced Meiyu-Baiu rainfall in early summer 2020: Aftermath of the 2019 super IOD event. Geophys. Res. Lett., 47, e2020GL090671, https://doi.org/10.1029/2020GL090671. |
Tao, S. Y., and L. X. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C. P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60−92. |
Wang, B., J. Liu, J. Yang, T. J. Zhou, and Z. W. Wu, 2009: Distinct principal modes of early and late summer rainfall anomalies in East Asia. J. Climate, 22(13), 3864−3875, https://doi.org/10.1175/2009JCLI2850.1. |
Wang, B., B. Q. Xiang, and J. Y. Lee, 2013: Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions. Proceedings of the National Academy of Sciences of the United States of America, 110(8), 2718−2722, https://doi.org/10.1073/pnas.1214626110. |
Wang, J., Y. J. Liu, Y. H. Ding, and Z. L. Wu, 2021: Towards influence of Arabian Sea SST anomalies on the withdrawal date of Meiyu over the Yangtze-Huaihe River basin. Atmospheric Research, 249, 105340, https://doi.org/10.1016/j.atmosres.2020.105340. |
Wang, L., T. Li., L. Chen, S. K. Behera, and T. Nasuno, 2018: Modulation of the MJO intensity over the equatorial western pacific by two types of El Niño. Climate Dyn., 51, 687−700, https://doi.org/10.1007/s00382-017-3949-6. |
Wang, W. Q., M.-P. Hung, S. J. Weaver, A. Kumar, and X. H. Fu, 2014: MJO prediction in the NCEP Climate Forecast System version 2. Climate Dyn., 42, 2509−2520, https://doi.org/10.1007/s00382-013-1806-9. |
Wei, Y. T., and H. L. Ren, 2019: Modulation of ENSO on fast and slow MJO modes during boreal winter. J. Climate, 32, 7483−7506, https://doi.org/10.1175/JCLI-D-19-0013.1. |
Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 1917−1932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2. |
Wu, P., Y. H. Ding, and Y. J. Liu, 2017: A new study of El Niño impacts on summertime water vapor transport and rainfall in China. Acta Meteorologica Sinica, 75, 371−383, https://doi.org/10.11676/qxxb2017.033. (in Chinese with English abstract |
Wu, R. G., Z.-Z. Hu, and B. P. Kirtman, 2003: Evolution of ENSO-related rainfall anomalies in East Asia. J. Climate, 16, 3742−3758, https://doi.org/10.1175/1520-0442(2003)016<3742:EOERAI>2.0.CO;2. |
Wu, Z. H., and N. E. Huang, 2009: Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1, 1−41, https://doi.org/10.1142/S1793536909000047. |
Xie, P. P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates. Bull. Amer. Meteor. Soc., 78(11), 2539−2558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2. |
Xie, S. P., K. M. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-Western Pacific climate during the summer following El Niño. J. Climate, 22, 730−747, https://doi.org/10.1175/2008JCLI2544.1. |
Xue, Y., M. Y. Chen, A. Kumar, Z.-Z. Hu, and W. Q. Wang, 2013: Prediction skill and bias of tropical Pacific sea surface temperatures in the NCEP Climate Forecast System version 2. J. Climate, 26, 5358−5378, https://doi.org/10.1175/JCLI-D-12-00600.1. |
Yamaura, T., and T. Tomita, 2014: Two physical mechanisms controlling the interannual variability of Baiu precipitation. J. Meteor. Soc. Japan, 92, 305−325, https://doi.org/10.2151/jmsj.2014-403. |
Yoo, J. H. , A. W. Robertson, and I. S. Kang, 2010: Analysis of intraseasonal and interannual variability of the Asian summer monsoon using a hidden Markov model. Journal of Climate, 23(20), 5498−5516. |
Yuan, Y., H. Yan, and C.-Y. Li, 2014: Possible influences of the tropical Indian Ocean dipole on the eastward propagation of MJO. Journal of Tropical Meteorology, 20(2), 173−180. |
Zhang, L., W. Q. Han, and Z.-Z. Hu, 2021a: Interbasin and Multi-time-scale interactions in generating the 2019 extreme Indian Ocean dipole. J. Climate, 34, 4553−4566, https://doi.org/10.1175/JCLI-D-20-0760.1. |
Zhang, R. H., Q. Y. Min, and J. Z. Su, 2017: Impact of El Niño on atmospheric circulations over East Asia and rainfall in China: Role of the anomalous western North Pacific anticyclone. Science China Earth Sciences, 60, 1124−1132, https://doi.org/10.1007/s11430-016-9026-x. |
Zhang, W. J., Z. C. Huang, F. Jiang, M. F. Stuecker, G. S. Chen, and F.-F. Jin, 2021b: Exceptionally persistent Madden‐Julian oscillation activity contributes to the extreme 2020 East Asian summer monsoon rainfall. Geophys. Res. Lett., 48, e2020GL091588, https://doi.org/10.1029/2020GL091588. |
Zhang, Y., T. Li, J. Y. Gao, and W. Wang, 2020: Origins of quasi-biweekly and intraseasonal oscillations over the South China Sea and Bay of Bengal and scale selection of unstable equatorial and off-equatorial modes. Journal of Meteorological Research, 34, 137−149, https://doi.org/10.1007/s13351-020-9109-7. |
Zhou, Z. Q., S. P. Xie, and R. H. Zhang, 2021: Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions. Proceedings of the National Academy of Sciences of the United States of America, 118, e2022255118, https://doi.org/10.1073/pnas.2022255118. |